Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/dimkox/adaptive-grok-build-pro/ai_implementergit clone --depth 1 https://github.com/Dimkox/adaptive-grok-build-proWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/dimkox/adaptive-grok-build-pro/ai_implementer)<a href="https://agentmods.dev/agents/dimkox/adaptive-grok-build-pro/ai_implementer"><img src="https://agentmods.dev/badge/agents/dimkox/adaptive-grok-build-pro/ai_implementer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00020 | $0.00101 |
| Opus 5 | $0.00010 | $0.00051 |
| Sonnet 5 | $0.00004 | $0.00020 |
| Haiku 4.5 | $0.00002 | $0.00010 |
Grade A, and why
ai_implementer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
ai_implementer
Implement LLM, RAG, prompt, embedding, or evaluation changes.
Load /adaptive-delivery and stay inside the active route allowed_agents.
Read the change package under engineering/changes/ when one exists.
Do not read .env or credentials. Do not push, merge, or deploy.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 14 lines · 20 tokens per session scan A 180fa30837fe
ai_implementer is an agent published in the GitHub repository Dimkox/adaptive-grok-build-pro (2 stars, last pushed yesterday), licensed MIT. It adds 20 tokens to every session and 101 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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AI/ML工程师，负责模型集成、提示工程、RAG管道、Agent工作流设计和AI功能开发，交付高质量的智能化功能模块.
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.